CS 294-5: Statistical Natural Language Processing

1
AI Adjacent Fields

• Philosophy
• Logic, methods of reasoning
• Mind as physical system
• Foundations of learning, language, rationality
• Mathematics
• Formal representation and proof
• Algorithms, computation, (un)decidability,
  (in)tractability
• Probability and statistics
• Psychology
• Adaptation
• Phenomena of perception and motor control
• Experimental techniques (psychophysics, etc.)?
• Economics formal theory of rational decisions
• Linguistics knowledge representation, grammar
• Neuroscience physical substrate for mental
  activity
• Control theory
• homeostatic systems, stability
• simple optimal agent designs

This slide deck courtesy of Dan Klein at UC
Berkeley
2
(No Transcript)
3
Reflex Agents

• Reflex agents
• Choose action based on current percept (and maybe
  memory)?
• May have memory or a model of the worlds current
  state
• Do not consider the future consequences of their
  actions
• Act on how the world IS
• Can a reflex agent be rational?

4
Goal Based Agents

• Goal-based agents
• Plan ahead
• Ask what if
• Decisions based on (hypothesized) consequences of
  actions
• Must have a model of how the world evolves in
  response to actions
• Act on how the world WOULD BE

5
Search Problems

• A search problem consists of
• A state space
• A transition function
• A start state and a goal test
• A solution is a sequence of actions (a plan)
  which transforms the start state to a goal state

N, 1.0
E, 1.0
6
Example Romania

• State space
• Cities
• Transition function
• Go to adj city with cost dist
• Start state
• Arad
• Goal test
• Is state Bucharest?
• Solution?

7
(No Transcript)
8
(No Transcript)
9
Search Trees
E, 1.0
N, 1.0

• A search tree
• This is a what if tree of plans and outcomes
• Start state at the root node
• Children correspond to successors
• Nodes contain states, correspond to PLANS to
  those states
• For most problems, we can never actually build
  the whole tree

10
Another Search Tree

• Search
• Expand out possible plans
• Maintain a fringe of unexpanded plans
• Try to expand as few tree nodes as possible

11
General Tree Search

• Important ideas
• Fringe
• Expansion
• Exploration strategy
• Main question which fringe nodes to explore?

Detailed pseudocode is in the book!
12
(No Transcript)
13
(No Transcript)
14
States vs. Nodes

• Nodes in state space graphs are problem states
• Represent an abstracted state of the world
• Have successors, can be goal / non-goal, have
  multiple predecessors
• Nodes in search trees are plans
• Represent a plan (sequence of actions) which
  results in the nodes state
• Have a problem state and one parent, a path
  length, a depth a cost
• The same problem state may be achieved by
  multiple search tree nodes

Search Nodes
Problem States
Parent
Depth 5
Action
Node
Depth 6
15
(No Transcript)
16
(No Transcript)
17
(No Transcript)
18
(No Transcript)
19
(No Transcript)
20
(No Transcript)
21
Comparisons

• When will BFS outperform DFS?
• When will DFS outperform BFS?

22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
(No Transcript)
26
(No Transcript)
27
Uniform Cost Search

• What will UCS do for this graph?
• What does this mean for completeness?

b
0
1
0
START
a
1
GOAL
28
Uniform Cost Issues

• Remember explores increasing cost contours
• The good UCS is complete and optimal!
• The bad
• Explores options in every direction
• No information about goal location

c ? 1

c ? 2
c ? 3
Start
Goal
29
(No Transcript)
30
Heuristics
31
Best First / Greedy Search

• Expand the node that seems closest
• What can go wrong?